Learning sequence-function relationships with scalable, interpretable Gaussian processes.

ABSTRACT: Understanding the relationship between biological sequences, such as DNA, RNA or protein sequences, and their resulting phenotypes is one of the central goals of genetics. This task is complicated by epistasis, i.e., the context dependence of mutational effects. Advances in high-throughput phenotyping now make it possible to study these relationships at unprecedented scale, generating large datasets that measure phenotypes for tens or hundreds of thousands of sequences. However, standard regression models for analyzing such datasets often make unrealistic assumptions about the generalizability of mutational effects and epistatic coefficients across genetic backgrounds. Deep neural networks offer greater flexibility but suffer from limited interpretability and lack uncertainty quantificatio

SUBMITTER: Zhou J 

PROVIDER: S-EPMC12393447 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature